Search Method Journal is an independent English-language editorial publication about SEO, content and modern search systems. Our central question is simple: how do we know what we think we know about SEO?
What we study
We examine how websites are discovered, crawled, rendered and indexed; how content addresses reader needs; how links are measured; and how search interfaces change the paths people take to information. Analytics and research methods connect those topics: an attractive explanation is useful only when its evidence and limits are clear.
The publication is for editors, developers, analysts and website owners who need to make defensible decisions. The aim is to help readers diagnose a problem, evaluate a claim or design a better investigation.
Evidence has different strengths
Official documentation describes a platform’s stated behavior. Direct measurements describe what a particular collection method captured. Third-party estimates depend on a provider’s sample and model. Observations show what happened under recorded conditions. A hypothesis proposes an explanation that still needs testing.
We keep those categories visible. A correlation is not presented as proof of causation, and a proprietary SEO score is not presented as a search engine’s internal metric. Examples and proposed experiments are labeled so they cannot be mistaken for research we conducted.
Editorial independence
Search Method Journal publishes explanatory resources. We do not offer agency services or promise rankings. References to products and providers identify the tools or data being discussed; they are not evidence of a commercial relationship. Any future sponsorship or affiliate relationship must be disclosed alongside the affected material.
Start with a question
- Is the explanation supported? Start with SEO Research Methods.
- Where is the problem occurring? Use Search Diagnostics.
- What does the number mean? Read SEO Metrics Explained.
- Is a term unclear? Consult the SEO Glossary.
Our Editorial Standards describe sourcing, uncertainty, corrections and AI-assisted work.